模型:
lambdalabs/sd-naruto-diffusers
Stable Diffusion fine tuned on Naruto by Lambda Labs .
Try the live text-to-naruto demo here ! If you want more details on how to train your own Stable Diffusion variants, see this example .
Put in a text prompt and generate your own Naruto style image!
Game of Thrones to Naruto
Marvel to Naruto
We find that prompt engineering does help produce compelling and consistent Naruto style portraits. For example, writing prompts such as ' person_name ninja portrait' or ' person_name in the style of Naruto' tends to produce results that are closer to the style of Naruto character with the characteristic headband and other elements of costume.
Here are a few examples of prompts with and without prompt engineering that will illustrate that point.
Without prompt engineering
With prompt engineering
A cute bunny:
Without prompt engineering
With prompt engineering
To run model locally:
!pip install diffusers==0.3.0 !pip install transformers scipy ftfy
import torch from diffusers import StableDiffusionPipeline from torch import autocast pipe = StableDiffusionPipeline.from_pretrained("lambdalabs/sd-naruto-diffusers", torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "Yoda" scale = 10 n_samples = 4 # Sometimes the nsfw checker is confused by the Naruto images, you can disable # it at your own risk here disable_safety = False if disable_safety: def null_safety(images, **kwargs): return images, False pipe.safety_checker = null_safety with autocast("cuda"): images = pipe(n_samples*[prompt], guidance_scale=scale).images for idx, im in enumerate(images): im.save(f"{idx:06}.png")
Trained on BLIP captioned Naruto images using 2xA6000 GPUs on Lambda GPU Cloud for around 30,000 step (about 12 hours, at a cost of about $20).
Trained by Eole Cervenka after the work of Justin Pinkney ( @Buntworthy ) at Lambda Labs .